Description

This is the report of the analysis made for the paper (TITLE) AND AUTHORS. INSERT ABSTRACT

Importing data

Importing data and filtering out those genes with cpm lesser than 1. We use the filtered.data method in NOISeq package.

countMatrix <- ReadDataFrameFromTsv(file.name.path="../../data/refSEQ_countMatrix.txt")
## ../../data/refSEQ_countMatrix.txt read from disk!
# head(countMatrix)

designMatrix <- ReadDataFrameFromTsv(file.name.path="../../design/all_samples_short_names_noRS2HC7.tsv")
## ../../design/all_samples_short_names_noRS2HC7.tsv read from disk!
# head(designMatrix)

filteredCountsProp <- filterLowCounts(counts.dataframe=countMatrix, 
                                    is.normalized=FALSE,
                                    design.dataframe=designMatrix,
                                    cond.col.name="gcondition",
                                    method.type="Proportion")
## features dimensions before normalization: 27179
## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3

Plot PCA of log unnormalized data

PCA Plot of filtered not-normalized data.

PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), 
    design.matrix=designMatrix, 
    shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2", 
    plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")
## [1] TRUE

Control Genes

Negative control genes

Loading Negative Control Genes to normalize data

library(readxl)

sd.ctrls <- read_excel(path="../../data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]

sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]

sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]

int.neg.ctrls <- sd.neg.ctrls
int.neg.ctrls <- unique(int.neg.ctrls)
neg.map <- convertGenesViaMouseDb(gene.list=int.neg.ctrls, fromType="SYMBOL",
                                "ENTREZID")
# sum(is.na(neg.map$ENTREZID))
neg.ctrls.entrez <- as.character(neg.map$ENTREZID)


ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]

positive control genes

Loading Positive Control Genes to detect them during the differential expression step.

## sleep deprivation
sd.lit.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx", 
                        sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]


sd.est.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx", 
                        sheet=3)

sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))

sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]

Normalizations

TMM Normalization

Normalizing data with TMM, as implemented in edgeR package, and plotting a PCA and an RLE plot of them.

normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp, 
                                    norm.type="tmm", 
                                    design.matrix=designMatrix)

PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), 
                    design.matrix=designMatrix, shapeColname="condition", 
                    colorColname="genotype", xPCA="PC1", yPCA="PC2", 
                    plotly.flag=TRUE, show.plot.flag=TRUE, 
                    prefix.plot="TMM-Norm")
## [1] TRUE
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])

TMM + RUVs Normalization

Applying a RUVs method of RUVSeq package on normalized data, in order to adjust the counts for the unwanted variation. And of corse we plot a PCA and an RLE plot on these data.

library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
                       scIdx=groups, k=5)

normExprData <- ruvedSExprData$normalizedCounts

ggp <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), 
                    design.matrix=designMatrix, shapeColname="condition", 
                    colorColname="genotype", xPCA="PC1", yPCA="PC2", 
                    plotly.flag=FALSE, show.plot.flag=FALSE, save.plot=FALSE,
                    prefix.plot=NULL)
## [1] FALSE
ggplotly(ggp)
dir.create("plots")
save_plot(filename="plots/PCA.pdf", plot=ggp)

pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])

edgeR Differential Expression Analysis

Making differential expression analysis with edgeR package on four different contrasts.

Here is a brief legend: * WTHC5: Wild Type Home Cage Control 5 days * WTSD5: Wild Type Sleep Deprivation 5 days. * KOHC5: Knock Out Home Cage Control 5 days. * KOSD5: Knock Out Sleep Deprivation 5 days.

padj.thr <- 0.05
venn.padgj.thr <- 0.1
desMat <- cbind(designMatrix, ruvedSExprData$W)
colnames(desMat) <- c(colnames(designMatrix), colnames(ruvedSExprData$W))

cc <- c("WTSD5 - WTHC5", "KOHC5 - WTHC5",
        "KOSD5 - WTSD5", "KOSD5 - KOHC5")

rescList1 <- applyEdgeR(counts=filteredCountsProp, design.matrix=desMat,
                        factors.column="gcondition", 
                        weight.columns=c("W_1", "W_2", "W_3", "W_4", "W_5"),
                        contrasts=cc, useIntercept=FALSE, p.threshold=1,
                        is.normalized=FALSE, verbose=TRUE)
names <- names(rescList1)
rescList1 <- lapply(seq_along(rescList1), function(i) 
{
    attachMeans(normalized.counts=normExprData, design.matrix=desMat, 
                factor.column="gcondition", contrast.name=names(rescList1)[i],
                de.results=rescList1[[i]])
})
names(rescList1) <- names

Wild Type Sleep Deprivation VS Wild Type Home Cage Control

An histogram of pvalues.

PlotHistPvalPlot(de.results=rescList1[[1]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[1])

volcano plot

A volcano plot of differential expressed genes.

res.o.map1 <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[1]]), 
                            fromType="ENTREZID")

res.o <- attachGeneColumnToDf(mainDf=rescList1[[1]],
                                genesMap=res.o.map1,
                                rowNamesIdentifier="ENTREZID",
                                mapFromIdentifier="ENTREZID",
                                mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[1], "_edgeR"))

vp <- luciaVolcanoPlot(res.o, sd.pos.ctrls, prefix=names(rescList1)[1], 
                    threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[1]
ddetable <- detable

tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.df <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
colnames(pos.df) <- c("total_p.ctrl", "p.ctrl_de_mapped", 
                    "p.ctrl_notde_mapped")
rownames(pos.df) <- names(rescList1)[1]

wt <- res.o[which(res.o$FDR < padj.thr),]
wt.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]

Knock Out Home Cage control VS Wild Type Home Cage Controls

pvalue histogram

An histogram of pvalues.

PlotHistPvalPlot(de.results=rescList1[[2]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[2])

volcano plot

A volcano plot of differential expressed genes.

rs2.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[2]]), 
                            fromType="ENTREZID")

res.rs2.o <- attachGeneColumnToDf(mainDf=rescList1[[2]],
                                genesMap=rs2.o.map,
                                rowNamesIdentifier="ENTREZID",
                                mapFromIdentifier="ENTREZID",
                                mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.rs2.o, 
                    file.name.path=paste0(names(rescList1)[2], "_edgeR"))

vp <- luciaVolcanoPlot(res.rs2.o, positive.controls.df=NULL, 
                       prefix=names(rescList1)[2], 
                       threshold=padj.thr)
ggplotly(vp)
de <- sum(res.rs2.o$FDR < padj.thr)
nde <- sum(res.rs2.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[2]
ddetable <- rbind(ddetable, detable)

kowthc5 <- res.rs2.o[which(res.rs2.o$FDR < padj.thr),]
kowthc5.sign.genes.entrez <- rownames(res.rs2.o)[which(res.rs2.o$FDR < venn.padgj.thr)]

Knock Out Sleed Deprivation VS Wild Type Sleep Deprivation

pvalue histogram

An histogram of pvalues.

PlotHistPvalPlot(de.results=rescList1[[3]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[3])

volcano plot

A volcano plot of differential expressed genes.

res.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[3]]), 
                            fromType="ENTREZID")

res.o <- attachGeneColumnToDf(mainDf=rescList1[[3]],
                                genesMap=res.o.map,
                                rowNamesIdentifier="ENTREZID",
                                mapFromIdentifier="ENTREZID",
                                mapToIdentifier="SYMBOL")

WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[3], "_edgeR"))

vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls, 
                        prefix=names(rescList1)[3], threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[3]
ddetable <- rbind(ddetable, detable)

tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[3]
pos.df <- rbind(pos.df, pos.dff)

kowtsd5 <- res.o[which(res.o$FDR < padj.thr),]
kowtsd5.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]

Knock Out Sleep Deprivation VS Knock Out Home Cage Control

pvalue histogram

An histogram of pvalues.

PlotHistPvalPlot(de.results=rescList1[[4]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[4])

volcano plot

A volcano plot of differential expressed genes.

res.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[4]]), 
                            fromType="ENTREZID")

res.o <- attachGeneColumnToDf(mainDf=rescList1[[4]],
                                genesMap=res.o.map,
                                rowNamesIdentifier="ENTREZID",
                                mapFromIdentifier="ENTREZID",
                                mapToIdentifier="SYMBOL")

WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[4], "_edgeR"))


vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls, 
                        prefix=names(rescList1)[4], threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[4]
ddetable <- rbind(ddetable, detable)


tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[2]
pos.df <- rbind(pos.df, pos.dff)

ko <- res.o[which(res.o$FDR < padj.thr),]
ko.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]

DE TABLE + Positive Controls table

We present a summarization of the results. The first table is a summarization on how many genes are Differentially Expressed. The second table explains on the first column how many positive controls we have, on the second column how many positive controls have been identified over the differentially expressed genes, and, finally, on the third column how many positive controls have beed identified on the NOT differentially expressed genes.

ddetable
##                 de   nde
## WTSD5 - WTHC5 5650  8804
## KOHC5 - WTHC5   39 14415
## KOSD5 - WTSD5   82 14372
## KOSD5 - KOHC5 5772  8682
pos.df
##               total_p.ctrl p.ctrl_de_mapped p.ctrl_notde_mapped
## WTSD5 - WTHC5          579              454                 102
## KOSD5 - WTSD5          579                4                 552
## KOHC5 - WTHC5          579              452                 104

Venn Diagram

KOHC5-WTHC5 vs KOSD5-WTSD5

We take the results of two contrasts. Knock Out Sleed Deprivation VS Wild Type Sleep Deprivation and Knock Out Home Cage control VS Wild Type Home Cage Controls . And plot the results in a Venn Diagram

source("../../R/venn2.R")
gene.map <- convertGenesViaMouseDb(gene.list=rownames(normExprData),
                                   fromType="ENTREZID", toType="SYMBOL")
venn <- Venn2de(x=kowthc5.sign.genes.entrez, y=kowtsd5.sign.genes.entrez, 
        label1="KOHC5_WTHC5", label2="KOSD5_WTSD5",
        title="KOHC5_WTHC5 venn KOSD5_WTSD5", plot.dir="./",
        conversion.map=gene.map)

Heatmaps

Union of genes

An heatmap of the union of the genes identified.

source("../../R/heatmapFunctions.R")
de.genes.entr <- union(rownames(venn$int), rownames(venn$XnoY))
de.genes.entr <- union(de.genes.entr, rownames(venn$YnoX))

gene.map <- convertGenesViaMouseDb(gene.list=de.genes.entr, 
                            fromType="ENTREZID")
de.genes.symb <- attachGeneColumnToDf(as.data.frame(de.genes.entr, 
                                                    row.names=de.genes.entr), 
                                    genesMap=gene.map, 
                                    rowNamesIdentifier="ENTREZID", 
                                    mapFromIdentifier="ENTREZID",
                                    mapToIdentifier="SYMBOL")

# de.genes.symb[which(is.na(de.genes.symb$gene)),]
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="100039826")]  <- "Gm2444" ## not annotated in ncbi
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="210541")]  <- "Entrez:210541" ## not annotated in ncbi


de.genes.counts <- normExprData[match(de.genes.symb$de.genes.entr, rownames(normExprData)),]
rownames(de.genes.counts) <- de.genes.symb$gene

de.gene.means <- computeGeneMeansOverGroups(counts=de.genes.counts, 
                            design=designMatrix, groupColumn="gcondition")

library(gplots)
library(clusterExperiment)
color.palette = clusterExperiment::seqPal3#c("black", "yellow")
pal <- colorRampPalette(color.palette)(n = 1000)


# table(filter)


library(pheatmap)
filter2 <- rowMeans(de.gene.means)>0
ph1 <- pheatmap(log(de.gene.means[filter2,]+1), cluster_cols=FALSE, scale="row", color=pal, border_color=NA, fontsize_row=5)

save_pheatmap_pdf(filename="plots/heatmap_union_genes.pdf", plot=ph1)
## RStudioGD 
##         2

Subset of genes

Heatmap of a group of genes which present inverse trends between Wild Type and Knock Out conditions.

filter <- apply(de.gene.means, 1, function(x) log(x[4]/x[3]) * log(x[2]/x[1]) < 0)
filter[is.na(filter)] <- FALSE

ph2 <- pheatmap(log(de.gene.means[filter,]+1), cluster_cols=FALSE, scale="row", color=pal, border_color=NA, fontsize_row=8)

save_pheatmap_pdf(filename="plots/heatmap_genes_two_trends.pdf", plot=ph2)
## RStudioGD 
##         2

Heatmap genes profiles

The trends of the genes identified in the second heatmap.

source("../../R/plotGeneProfile.R")


g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Gm7984",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gm7984", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Mgat4b",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Mgat4b", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Ano6",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ano6", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Zbtb21",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Zbtb21", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Sfxn1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Sfxn1", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Plxnb2",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Plxnb2", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Gng4",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gng4", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Slc6a13",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Slc6a13", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Pdyn",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Pdyn", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Reln",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Reln", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Rpl29",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Rpl29", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Ryr1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ryr1", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Rnd1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Rnd1", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Plekha6",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Plekha6", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Mapk1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Mapk1", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Slc7a11",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Slc7a11", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Gprin1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gprin1", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Cttnbp2",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Cttnbp2", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Jmy",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Jmy", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Nek6",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nek6", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Dusp10",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Dusp10", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Zmynd19",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Zmynd19", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Kcnv1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Kcnv1", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Ddit4l",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ddit4l", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Fggy",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Fggy", "_gene_profile.pdf"), plot=g)

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Sytl1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Sytl1", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Bmp1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Bmp1", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Otof",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Otof", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Syt7",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Syt7", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Chia1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Chia1", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Tuba1c",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Tuba1c", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Olfr287",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Olfr287", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Gm7984",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gm7984", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Mgat4b",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Mgat4b", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Gprin1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gprin1", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Atp6v0c",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Atp6v0c", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Jun",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Jun", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Ddit4l",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Ddit4l", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Pdyn",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Pdyn", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Sema3a",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Sema3a", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Gng4",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gng4", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Reln",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Reln", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Kcnv1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Kcnv1", "_gene_profile.pdf"), plot=g)

Special genes profiles

Profiles of some known genes.

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Nr1d1",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nr1d1", "_gene_profile.pdf"), plot=g) 


g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Per3",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Per3", "_gene_profile.pdf"), plot=g) 

g <- geneProfileLucia(normalized.counts=normExprData,
            design.matrix=designMatrix,
            gene.name="Fabp7",
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Fabp7", "_gene_profile.pdf"), plot=g) 
# g <- geneGroupProfile(normalized.counts=normExprData, design.matrix=designMatrix,
            # gene.names=c("Nr1d1", "Fabp7", "Per3"),
            # res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
# ggplotly(g)